Stratified random sampling is a sampling method in which a population group is divided into one or many distinct units – called strata – based on shared behaviors or characteristics.
Stratification refers to the process of classifying sampling units of the population into homogeneous units. In stratified random sampling, any feature that explains differences in the characteristics of interest can be the basis of forming strata.
For example, people’s income or education level is a variation that can provide an appropriate backdrop for strata.
Stratified random sampling refers to a sampling technique in which a population is divided into discrete units called strata based on similar attributes. The selection is done in a manner that represents the whole population.
The sampling technique is preferred in heterogeneous populations because it minimizes selection bias and ensures that the entire population group is represented.
It is not suitable for population groups with few characteristics that can be used to divide the population into relevant units.
Understanding Stratified Random Sampling
Sampling a large population is often an underlying challenge in conducting statistical surveys. A more feasible approach to save time and money would be to pick a smaller group or sample size that would be used instead to represent the entire population.
A stratified random sampling approach divides the population into relevant strata to increase a certain population group’s representativeness. However, it is only achievable if relevant strata are known and distinguishable in a population group.
In stratified random sampling, a researcher selects a small sample size with similar characteristics to represent a population group under study. A population being studied in a survey may be too large to be analyzed individually; hence, it is organized into groups with the same features to save costs and time.
The technique offers wide usage, such as estimating the income for varying populations, polling of elections, and life expectancy.
How Random Stratified Sampling Works
A researcher can select a more feasible approach to study an extremely large population. An analysis is forced to divide the population into relevant strata before sampling.
One of the ways researchers use to select a small sample is called stratified random sampling. Estimates generated within strata are more precise than those from random sampling because dividing the population into homogenous groups often reduces sampling error and increases precision.
When seeking a potential stratum, it is always advisable to seek one that best minimizes variation in the characteristics under investigation and maximizes variation among strata. Stratified random sampling is best used with a heterogeneous population that can be divided using ancillary information.
Simple Random Sampling vs. Stratified Random Sampling
1. Sampling the population
Simple random sampling – sometimes known as random selection – and stratified random sampling are both statistical measuring tools. Using random selection will minimize bias, as each member of the population is treated equally with an equal likelihood of being sampled.
In contrast, stratified random sampling breaks the population into distinct subgroups called strata that have similar attributes. A random sample is taken from each stratum, with the sample size proportional to its stratum size compared to the population. This will ensure that the sample will highlight the differences between stratum groups.
Both simple and stratified random sampling entails sampling without replacement since they do not allow each case’s sample back into the sampling frame.
2. Robustness in sample selection
Overall, simple random sampling is more robust than stratified random sampling, especially when a population has too many differences to be categorized.
Simple random sampling is also effective in situations where a population has little information that will not allow it to be subdivided into distinct units.
For instance, an online retail store may wish to survey its online customers’ purchasing habits to determine the future of its product line. If the store has approximately 50,000 customers, it may select 500 of these customers as the random sample. 500 is the sample frame within which customers are sampled purely at random.
To ensure the number of customers falls within the required range, the repeated selection is replaced. The retail store may then apply the estimated characteristics to the rest of the customers.
It can, therefore, be said that the chosen sample represents the entire customer population of 50,000. In this sense, a simple random sampling analyzes a more evenly dispersed sample throughout the population.
Strengths and Weaknesses of Stratified Random Sampling
Stratified random sampling captures the key attributes of a population group. As a result, it produces characteristics in the sample that are proportional to the entire population. Therefore stratified random sampling provides a higher degree of precision than simple random sampling.
Stratified random sampling is not suitable for every survey. It only works under the condition where a population can be stratified using relevant attributes and that the subgroups are clearly defined and do not overlap. Subjects that fall into multiple groups have a higher likelihood of being chosen and may cause a misrepresented sample.
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